Updates

Model and report changes

  1. The definition of deaths has been adapted to include all deaths that occur in individuals who have had lab-confirmed infection within 60 days from the date of their most recent positive test. This definition reflects more realistically the burden of COVID-19.
  2. Using observations of improved survival in hospitalised COVID-19 patients, we have allowed the probability of dying following infection with SARS-CoV2 (the infection-fatality rate, IFR) to gradually change over the course of June 2020, with a decrease being estimated.
  3. The model uses seroprevalence data on the presence of COVID-19 antibodies in blood samples taken by NHSBT to estimate the levels of cumulative infection within the population over time. As, from early June, the NHSBT has been giving a constantly declining prevalence of antibodies, and these data have been curtailed at this point.
  4. The modelling now accounts for a different susceptibility to infection in the under-15s, using information from literature (Viner et al, 2020) suggesting that children less likely to acquire infection when in contact with an infectious individual.
  5. The geographical definition has been changed from the seven NHS regions (map) to the nine regions typically used in government (map). This new spatial definition more appropriately reflects the existing regional heterogeneity.

Updated findings

  1. The current estimate of the daily number of new infections occurring each day across England is 91,100 (68,700–119,000, 95% credible interval).
  2. The daily number of new infections is particularly high in the East of England (EE), London and the South-East (SE) with 14,500, 16,000 and 17,200 daily new infections, respectively. These correspond to 234, 180 and 188 per 100,000 population. Note that a substantial proportion of these daily infections will be asymptomatic.
  3. We predict that the number of deaths occurring daily is likely to be between 619 and 899 on the 1st January 2021.
  4. We estimate Rt to be above 1 in most regions. Rt is with certainty above 1 in the EE, SE. The probability of Rt exceeding 1 is 99%, 98% and 93% in the South West (SW) London, and North West (NW), respectively; 85% in the East Midlands (EM); 61% in the North East (NE); 40% in the West Midlands (WM); and 34% in the Yorkshire and Humber(Y&H).
  5. The growth rate for England is now estimated to be 0.02 (0.02–0.03, 95% credible interval) per day. This means that, nationally, the number of infections has started to grow again, with some evidence of plateauing only in the WM and Y&H.
  6. London, followed by the NW and the WM continues to have the highest attack rate, that is the proportion of the population who have ever been infected, with 21%, 20% and 20% respectively. The SW continues to have the lowest attack rate at 6%.
  7. Note that the deaths data used are only very weakly informative on Rt over the last two weeks. Therefore, the estimate for current incidence, Rt and the forecast of daily numbers of deaths are likely to be subject to some revision.

Interpretation

The plots of Rt over time have started to show evidence of an increase, following a period of downward trend during Autumn. This results in most of the Rt values being around 1. Rt is certainly above 1 in the SE and EE, and almost certainly above 1 in the SW, London, and NW.

As a result, the number of infections has definitely increased with strong growth in the SE, EE and SW, moderate growth in London, a resurgence in the EM, NE and NW, and a potential plateauing in the WM and YH.

Incidence of deaths levelled off during the last week of November / first week of December, with some falls noted in the North East, North West, and Yorkshire & Humberside, but are expected to start to climb significantly throughout December and early January in all regions.

It is now possible to estimate that the latest lock-down has contributed to the continuation of the downwards trends in Rt and the slowing down in the growth in the number of infections. This contribution appears quite modest and now these trends have reverted in most regions, in particular in the SE, EE, London and SW. The impact of the increased restrictions announced on Saturday 19th December (Tier 4) cannot yet be measured, and therefore any impact from them are excluded from these results.

Summary

Real-time tracking of an epidemic, as data accumulate over time, is an essential component of a public health response to a new outbreak. A team of statistical modellers at the MRC Biostatistics Unit (BSU), University of Cambridge, are working to provide regular now-casts and forecasts of COVID-19 infections and deaths. This information feeds directly to the SAGE sub-group, Scientific Pandemic Influenza sub-group on Modelling (SPI-M), and to regional Public Health England (PHE) teams.

Methods

We fit a transmission model (Birrell et al. 2020) to a number of data sources (see ‘Data Sources’), to reconstruct the number of new COVID-19 infections over time in different age groups and NHS regions, estimate a measure of ongoing transmission and predict the number of new COVID-19 deaths.

Data sources

We use:

  1. Data on COVID-19 confirmed deaths from the Public Health England (PHE) line-listing This consists of a combination of deaths notified to:
    • the Demographics Batch Service (DBS), a mechanism that allows PHE to submit a file of patient information to the National Health Service spine for tracing against the personal demographics service (PDS). PHE submit a line list of patients diagnosed with COVID-19 to DBS daily. The file is returned with a death flag and date of death updated (started 20th March, 2020).
    • NHS England, who report data from NHS trusts relating to patients who have died after admission to hospital or within emergency department settings.
    • Health Protection Teams (HPTs), resulting from a select survey created by PHE to capture deaths occurring outside of hospital settings, e.g. care homes (started 23rd March, 2020)
  2. Data on antibody prevalence in blood samples from a PHE survey of NHS Blood Transfusion (NHSBT) donors.

Data are stratified into eight age groups: <1, 1-4, 5-14, 15-24, 25-44, 45-64, 65-74, 75+, and the NHS England regions (North East and Yorkshire, North West, Midlands, East of England, London, South East, South West).

  1. Published information on the the natural history of COVID-19 (Verity et al., 2020; Li et al, 2020)
  2. Information on contacts between different age groups from:
    • A Survey that describes relative rates of contacts between different age groups (Mossong et al. 2008).
    • Google Community Mobility reports, informing the changes in people’s mobility over the course of the pandemic, particularly after the March 23rd lockdown measures.
    • The ONS’ time use survey, which in conjunction with the google mobility study, allows estimation of the changing exposure to infection risk over time.
    • Data from the Department for Education describing the proportion of children currently attending school.

Epidemic summary

Current \(R_t\)

Value of \(R_t\), the average number of secondary infections due to a typical infection today.

Number of infections

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Attack rate

The percentage of a given group that has been infected.

By region

By age

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IFR

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Change in infections incidence

Growth rates

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NB: negative growth rates are rates of decline. Values are daily changes.

Region Median 95% CrI (lower) 95% CrI (upper)
England 0.02 0.02 0.03
East of England 0.05 0.03 0.06
East Midlands 0.01 -0.01 0.03
London 0.02 0.00 0.04
North East 0.00 -0.02 0.03
North West 0.01 0.00 0.03
South East 0.04 0.02 0.05
South West 0.03 0.01 0.05
West Midlands 0.00 -0.02 0.01
Yorkshire and The Humber 0.00 -0.02 0.01

Halving times

Halving times in days, if a region shows growth than value will be NA.

Region Median 95% CrI (lower) 95% CrI (upper)
England NA NA NA
East of England NA NA NA
East Midlands NA 90.61 NA
London NA NA NA
North East NA 37.04 NA
North West NA 238.78 NA
South East NA NA NA
South West NA NA NA
West Midlands 480.50 30.85 NA
Yorkshire and The Humber 263.58 31.20 NA

Doubling times

Doubling times in days, if a region shows decline then the value will be NA.

Region Median 95% CrI (lower) 95% CrI (upper)
England 30.08 22.14 45.68
East of England 14.98 11.11 24.65
East Midlands 68.94 27.26 NA
London 36.23 19.57 2491.48
North East 155.30 27.65 NA
North West 49.14 23.61 NA
South East 18.11 13.34 30.11
South West 26.47 15.35 125.56
West Midlands NA 47.35 NA
Yorkshire and The Humber NA 48.24 NA

Change in deaths incidence

Growth rates

NB: negative growth rates are rates of decline. Values are daily changes.

Region Median 95% CrI (lower) 95% CrI (upper)
England 0.03 0.02 0.04
East of England 0.06 0.05 0.08
East Midlands 0.02 0.00 0.04
London 0.04 0.02 0.06
North East 0.01 -0.02 0.03
North West 0.02 0.00 0.04
South East 0.05 0.04 0.07
South West 0.03 0.01 0.05
West Midlands 0.02 0.00 0.03
Yorkshire and The Humber 0.00 -0.02 0.02

Halving times

Halving times in days, if a region shows growth than value will be NA.

Region Median 95% CrI (lower) 95% CrI (upper)
England NA NA NA
East of England NA NA NA
East Midlands NA NA NA
London NA NA NA
North East NA 36.94 NA
North West NA 416.65 NA
South East NA NA NA
South West NA NA NA
West Midlands NA NA NA
Yorkshire and The Humber NA 38.18 NA

Doubling times

Doubling times in days, if a region shows decline then the value will be NA.

Region Median 95% CrI (lower) 95% CrI (upper)
England 22.70 18.31 29.68
East of England 10.74 8.29 15.30
East Midlands 33.84 18.09 367.17
London 18.80 12.42 40.37
North East 118.11 22.41 NA
North West 39.07 18.99 NA
South East 13.23 10.16 19.73
South West 22.06 12.91 81.82
West Midlands 41.11 20.67 17009.41
Yorkshire and The Humber 352.40 33.89 NA

Infections and deaths

The blue lines is show when interventions have been introduced (lockdown on 23 Mar and the relaxation of measures on 11 May), and the red line shows the date these results were produced (18 Dec).

Infection incidence

By region

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By age

Cumulative infections

By region

By age

Deaths incidence

By region

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By age

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Cumulative deaths

By region

By age

Prob \(R_t > 1\)

The figure below shows the probability that \(R_t\) is greater than 1 (ie: the number of infections is growing) in each region over time. Clicking the regions in the legend allows lines to be added or removed from the figure.

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\(R_t\)

Copyright © MRC Biostatistics Unit, University of Cambridge